Intrusion detection with directional sensing

ABSTRACT

A method and system of intrusion detection, which includes displaying sensing device output data as one or more images of a field of view of a sensing device ( 14.1, 14.2, 14.3 ). Through interaction with a graphical user interface: a user input to define and display an image detection area in relation to the one or more images of the field of view is received, wherein the image detection area is correlated to a detection area to be monitored by the sensing device ( 14.1, 14.2, 14.3 ) for intrusion detection. One or more user inputs that define a detection vector in the image detection area are received, the detection vector specifying a predetermined minimum displacement and direction of displacement in the field of view to be monitored. A graphical representation of the detection vector may be displayed on the image detection area. The method further includes detecting a target object and tracking the target object at least partially through the detection area and determining whether the progressive displacement of the target object has a component which exceeds the minimum displacement in the direction of the detection vector.

CLAIM OF PRIORITY

This application claims the benefit of priority of U.S. ProvisionalPatent Application Ser. No. 61/947,335, filed on Mar. 3, 2014, and thebenefit of priority of Australia Patent Application No. 2014901121,filed on Mar. 28, 2014, the benefit of priority of which is claimedhereby, and which are incorporated by reference herein in theirentirety.

FIELD OF THE INVENTION

The present invention generally relates to a security system fordetecting intruders at a site being monitored. More specifically, theinvention relates to a system and method of directional detectionemploying video capture.

BACKGROUND OF THE INVENTION

Video motion detection (VMD) is frequently used in the current securityindustry for detecting intruders entering a site, with predeterminedscenes of interest. VMD is performed by computer software on a sequenceof digital images captured by a video camera that is monitoring aparticular scene of interest. Each image in the sequence is composed ofan array of picture elements (pixels). Targets such as intruderstypically show up in an image as a different intensity to the backgroundscene in a captured image. VMD uses this in order to detect intruders inthe sequence by looking for changes in pixel intensities that areconsistent with a target moving through the scene. Groups of pixelsassociated with a target are also tracked from frame to frame todetermine the direction of motion. If the scene is calibrated, the sizeof the target, the distance it has traveled, and the speed of its travelcan be estimated from the tracked group of pixels. By ignoring targetsthat do not meet size, speed, and distance traveled criteria, therelated security systems can be tuned to detect human and vehiclemovement while rejecting small animal and foliage movement. Furthermore,a current VMD system is typically set up by defining which parts of thescene are to be sensitive to movement. By specifying large areas of thescene as sensitive, a highly effective detection system can beestablished in which intruders may be detected anywhere within thosedetection areas. Careful placement of these detection areas can alsoavoid false alarms from areas of no interest, such as car-parks orstreets adjacent an area of interest.

One of the drawbacks of existing VMD systems is that existing systemsare sensitive to all changes of intensity in the scene of interest. Itis therefore possible to potentially detect the shadows and headlightsof moving vehicles. If these shadows or headlight beams enter adetection area, and their movement meets the detection criteria, suchmovement will be wrongly detected as targets, even if the vehiclesthemselves are outside the detection area. This type of false alarmcommonly occurs when vehicles travel down a road that is outside of, butruns parallel to, the boundary of an area being protected.

In more recent VMD systems, trigger lines have been adopted to reducefalse alarms. With these systems the target has to cross the triggerline to cause an alarm, which reduces the likelihood of a false alarm bythe specificity of the line placement. However, this detection methodmay also be undesirable because the trigger line must be carefullyplaced to ensure that all possible real target trajectories will crossthe trigger line. Furthermore, in non-ideal conditions, it is possiblethat the target track will be lost and restarted as the target movesthrough the scene. If the target crosses the trigger line while notbeing tracked, no alarm will be raised. To reduce the effect of thispossibility multiple, parallel trigger lines are often needed.Irrespective, the use of single or multiple trigger lines also does notprevent false alarms caused by shadows and headlight beams because thesetarget tracks may still cross the necessary trigger lines due to the waythe beams or shadow changes as the vehicle passes adjacent to the areaof interest.

Reference to any prior art in the specification is not an acknowledgmentor suggestion that this prior art forms part of the common generalknowledge in any jurisdiction or that this prior art could reasonably beexpected to be understood, regarded as relevant, and/or combined withother pieces of prior art by a skilled person in the art.

SUMMARY OF THE INVENTION

In one aspect, the invention broadly provides for a method and systemthat allow for the input of a detection vector which is associated withan image detection area identified on a video image received from asensing device. The detection vector specifies a predetermined minimumdisplacement and direction of displacement in a detection area monitoredby the sensing device, and is displayed as a graphical representation onthe image detection area. The method and system extend to detecting andtracking a target object at least in part in the detection area, and ifit is determined that the progressive displacement of the target objecthas a component which exceeds the minimum displacement in the directionof the detection vector, an alarm condition is activated.

According to a first aspect of the invention there is provided a methodof intrusion detection, the method including:

-   -   displaying sensing device output data as one or more images of a        field of view of a sensing device;    -   through a graphical user interface:        -   receiving user input to define and display an image            detection area in relation to the one or more images of the            field of view, wherein the image detection area is            correlated to a detection area to be monitored by the            sensing device for intrusion detection;        -   receiving user input that defines a detection vector            specifying a predetermined minimum displacement and            direction of displacement in the field of view to be            monitored wherein a graphical representation of the            detection vector is (overlayed and) displayed on the image            detection area;    -   detecting a target object; and    -   tracking the target object at least partially through the        detection area and determining whether the progressive        displacement of the target object has a component which exceeds        the minimum displacement in the direction of the detection        vector.

The method may further include activating an alarm condition if thetarget object's displacement has a component which is determined toexceed the minimum displacement in the direction of the detectionvector, wherein either an initial position of the target object or theposition of the target object where the minimum displacement isexceeded, is within the detection area.

Preferably, the method may further include activating an alarm conditionif the target object's displacement is only within the detection areaand the target object's displacement exceeds the minimum displacement.

The component of displacement in the direction of the detection vectormay be determined with the use of the equation:

$I = \frac{\overset{\rightarrow}{S} \cdot \overset{\rightarrow}{D}}{D}$

where I is the component of displacement in the direction of thedetection vector;

vector {right arrow over (S)} represents a distance and direction of thetarget object as tracked through the detection area in athree-dimensional area; and

vector {right arrow over (D)} represents the detection vector.

The tracking of the target object may therefore start either from theinitial position where displacement is detected or from the positionwhere the target object is detected within the target area for the firsttime.

The sensing device may be a surveillance camera or the like.

The image detection area is a surface in a three-dimensional space ontowhich object movements in the corresponding two-dimensional image areprojected and interpreted.

The detection area may be defined as directional by specifying thedetection vector in relation to the detection area.

Optionally, the detection vector may be specified as bidirectional,wherein an alarm condition is activated when it is determined that theprogressive displacement of the target object exceeds the minimumdisplacement in either direction of the vector.

The graphical representation of the detection vector may be overlaid onthe image detection area as a flat arrow on a two-dimensional imageplane. The end points of this flat arrow detection vector may then bemapped to corresponding points in the detection area monitored by thesensing device, thereby to define the direction and minimum displacementin the detection area monitored.

Alternatively, the graphical representation of the detection vector maybe overlaid on the image detection area as a three-dimensional arrowdetection vector within the detection area monitored, wherein theposition of the end points of the three-dimensional area may then becorrelated with the image detection area, thereby to enable therendering of the detection vector in the correct perspective.

The user inputs defining the detection vector may be received throughone or more inputs of a pointing device on the graphical user interface,one or more inputs through a touch screen, one or more inputs ofcoordinates of the detection vector, an input of an angle of directionwith relation to an axis, and/or an input of a numerical value.

For example, the displacement and/or direction of the detection vectormay be defined or adjusted through manipulation of the arrows via thegraphical user interface. For example, a user may change the directionof the detection vector by clicking and dragging the end-points of thearrow with a click and point device, such as a computer mouse on adisplay.

Alternatively, the displacement and/or direction of the detection vectormay be defined or adjusted by receiving a numerical value for the lengthand/or angle of direction of the detection vector from a user throughthe graphical user interface. For example, the angle may be specifiedwith respect to an x or y axis of a two-dimensional image plane.Alternatively, the angle may be specified in terms of a navigationalbearing in the three-dimensional space. The method may then furtherinclude rendering the detection vector as an arrow at that angle on theimage detection area.

The direction of the detection vector may additionally be determined asperpendicular to a particular edge of the detection area, after the userhas selected the particular detection area edge through the graphicaluser interface.

The length of the graphical representation of the detection vector asdisplayed on the image detection area may not to be scale.

The step of tracking the target object at least partially through thedetection area may include estimating a target object path from discreetdetections of the target object within the field of view, preferablywithin the detection area, of the sensing device. The step of estimatingthe target object path may further include predicting various sectionsof the target object path. For example, the estimation step may includeone or more of the following:

-   -   predicting the target object path prior to the initial detection        of the target object by back-predicting where the target object        may have entered the field of view to the position of initial        detection;    -   predicting the target object path between discreet detections of        the target object; and    -   predicting the target object path between the final point of        detection of the target object in the field of view and where        the target object is likely to have left the field of view.

Any and all positions of the target object along the estimated targetobject path may provide a position of the target object to assess whenthe target object is in the detection area and for determining whetherthe progressive displacement of the target object exceeds the minimumdisplacement in the direction of the detection vector.

According to a further aspect there is provided a system including:

a user interface in order to receive inputs from a user of the system;

at least one processing unit and at least one memory for storinginstructions for execution by the at least one processing unit, theinstructions executed to:

-   -   display sensing device output data as one or more images of a        field of view of a sensing device;    -   through a graphical user interface:        -   receive user input to define and display an image detection            area in relation to the one or more images of the field of            view, wherein the image detection area is correlated to a            detection area to be monitored by the sensing device for            intrusion detection; and        -   receive user input that defines a detection vector            specifying a predetermined minimum displacement and            direction of displacement in the field of view to be            monitored, wherein a graphical representation of the            detection vector is displayed on the image detection area;    -   detect a target object; and    -   track the target object at least partially through the detection        area and determining whether the progressive displacement of the        target object has a component which exceeds the minimum        displacement in the direction of the detection vector.

The instructions may be executed to implement any of the other methodsteps as defined above.

As used herein, except where the context requires otherwise, the term“comprise” and variations of the term, such as “comprising”, “comprises”and “comprised”, are not intended to exclude further additives,components, integers or steps.

Further aspects of the present invention and further embodiments of theaspects described in the preceding paragraphs will become apparent fromthe following description, given by way of example and with reference tothe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated, by way of example only, with referenceto the accompanying drawings in which:

FIG. 1 shows a schematic diagram of an intrusion detection system formedby a central monitoring system and a number of sensing devices, inaccordance with an example embodiment;

FIG. 2 a shows an image coordinate view of a detection area;

FIG. 2 b shows the corresponding three-dimensional planar detection areaof the image coordinate view of FIG. 2 a;

FIG. 3 a shows an image coordinate view of a directional image detectionarea including an arrow representing a detection vector in accordancewith an example embodiment;

FIG. 3 b shows a view of a detection vector represented by an arrowdrawn to look as if placed on a three-dimensional planar detection area,in accordance with an example embodiment;

FIG. 4 shows graphically how the accumulated distance traveled in thedetection vector direction in three-dimensional coordinates may becomputed, in accordance with an example embodiment;

FIG. 5 a shows an example where an intruder in the form of an targetobject crosses the boundary of a detection area and travels towards amonitored property, where the target object is detected by the use ofdirectional sensing in accordance with an example embodiment;

FIG. 5 b shows how the same directional detection areas do not detectthe lights or shadows of a vehicle travelling parallel to the boundaryof a property being monitored in accordance with an example embodiment;

FIG. 6 shows how horizontal distances can be displayed when calibratingthe heights in an area of detection, in accordance with an exampleembodiment;

FIG. 7 shows how the entry and exit points of an object path as theobject passes through a detection area are determined, in accordancewith an example embodiment;

FIG. 8 is an example graphical user interface showing a field of view ofa sensing device that monitors an area with a road and an adjacentgravel area, wherein both a detection area and a detection vector havealready been inputted by a user, in accordance with an exampleembodiment;

FIG. 9 is a similar example graphical user interface as depicted in FIG.8, showing a field of view of a sensing device monitoring an officeenvironment, again with both a detection area and a detection vectoralready having been inputted by a user, in accordance with an exampleembodiment; and

FIG. 10 is a block diagram illustrating a computer processing system foruse as a central monitoring station 12, in accordance with an exampleembodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The present embodiments are directed to a method of and system forintrusion detection in which an operator of an intrusion detectionsystem, through the use of a graphical user interface, specifies adetection vector which is associated with a particular image detectionarea. The image detection area is selected by the operator through theuse of the graphical user interface, with the image detection areacorrelating to a detection area to be monitored by a sensing device ofthe intrusion system.

The detection vector, when associated with a detection area, representsa minimum displacement in a specific direction. The intrusion detectionsystem is configured to activate an alarm in the event that a targetobject, e.g., an intruder, is detected and tracked, it being determinedthat the progressive displacement of the target object has a componentwhich exceeds the minimum displacement in the direction of the detectionvector. In other words, when the target object moves at least a minimumdistance in the direction indicated by the vector, an alarm is raised.

The intrusion detection system 10 comprises, in one example embodiment,a central monitoring station 12 which is in communication with one ormore sensing devices that monitor a number of areas for various threatevents. At least some of the sensing devices are surveillance cameras,indicated by reference numerals 14.1, 14.2 and 14.3 that may be of thetype that provides video content, either as video streams or one or moreimages, to the central monitoring station 12. Thus, the surveillancecameras 14.1, 14.2 and 14.3 may be video cameras. Each of thesesurveillance cameras is commissioned to have a field of view, typicallyin an area close to an area with restricted access. The surveillancecameras communicate with the central monitoring system over a suitablenetwork 28.

The central monitoring station 12 typically comprises a server 16 anddata storage 18 in the form of one or more databases that storeinformation on sensing devices and the intrusion detection system. Thecentral monitoring station 12 also has one or more operator stations(two operator stations indicated by reference numerals 20.1 and 20.2)that are connected to the server 16 and databases 18 thereby to enablethe operators to access relevant data to enable intrusion detectionprocesses by the system. Each operator station includes one or more userinterfaces 22.1, 22.2 to display and receive information into thecentral monitoring system 12. In one example embodiment, the userinterface 22.1, 22.2 may be a display screen 24.1, 24.2 with a keyboard26.1, 26.2 and mouse (not shown). The user interface may also be a touchscreen, in some example embodiments.

The central monitoring station 12, either the server or the operatorstations, has one or more processing units and memory, for storinginstructions for execution by the processing units. The storedinstructions enable the central monitoring station 12 to perform variousfunctions and to process received data, as will be described in moredetail below. Although functionality is described with relation to thecentral monitoring station 12, it will be appreciated that all functionsneed not be performed by the central monitoring station 12 and that someof the processes could be executed on other devices. For example, someof the operations and functionality relating to conditions and alarmsmay be computed on intelligent surveillance cameras, in an intelligenthub, in a cloud service remote from the central monitoring station 12 orthe like.

Turning now to FIG. 2 a, a computer screen or display is shown todisplay an image of a field of view of a particular surveillance camera.The image is an output of the surveillance camera and is in this exampleembodiment transmitted to the central monitoring station 12. The imageis a two-dimensional representation of the field of view, and shows aroad 100, a building 101 and a detection area 102, also called the imagedetection area. In one example embodiment, the image detection area isdefined by an operator as an area which is specifically to be monitoredagainst intrusion. Thus, the purpose of the image detection area 102 isto detect intruders, also called target objects below, that might crossfrom the road 100 to the protected premises off-screen to the right ofthe detection area 102. The operator may define the image detection areathrough a graphical user interface, generated by the central monitoringstation 12, and once defined, the image detection area 102 is to bedisplayed in relation to the image of the field of view. For example, inFIG. 2 a, the image detection area 102 is indicated by cross-hatching.

FIG. 2 b shows graphically the correlation between the image which isdisplayed on the computer screen, i.e. a two-dimensional image 108 andthe field of view of the surveillance camera, i.e. a three-dimensionalscene 109. A road 103 that runs through the field of view is representedin the image 108 by the road 100, while the building 104 is representedby its image 101 in the image 108. The image detection area 102 enteredthrough the graphical user interface and displayed on the computerscreen as part of the image 108, corresponds to a virtualthree-dimensional planar detection area 105 in the three-dimensionalscene 109.

It can be seen from FIG. 2 b that the image detection area 102 asdisplayed on the computer screen and the virtual three-dimensionaldetection area 105 may not be in parallel planes, and that a line, suchas a detection vector described in more detail below, drawn on one mayappear at a different angle when viewed from the other. Furthermore, itwill be apparent that, as an object moves towards the building 104, i.e.further away into the scene, the image of that object in thetwo-dimensional image 108 will move up into the screen and appearsmaller. In order to correlate points between the two-dimensional image108 and the field of view of the surveillance camera, it is necessary todo some calibration of the system. For example, by recording thelocation of points in the field of view of the camera 109, on thethree-dimensional plane, and then correlating these locations with theircorresponding locations on the two-dimensional image 108 displayed onthe screen, mapping between the areas, in particular the two-detectionareas, is established. These points could, for example, be featurepoints 106 on the plane in the scene, or locations of a person 107 orother target moving around on the plane in the scene during calibration.The process of correlation between a two-dimensional image and athree-dimensional plane is described in more detail further below.

In order to define a detection area as directional, an operator is todefine a detection vector. In this example embodiment, the monitoringstation receives a user input, again through the graphical userinterface, that specifies the detection vector. The detection vector isspecified in relation to a particular area, i.e. the image detectionarea 102, and a graphical representation of the detection vector is thendisplayed on the image detection area on the screen of the monitoringstation. The detection vector specifies a predetermined minimumdisplacement and direction of displacement, which displacement anddirection in relation to a target object is used in the determination ofactivating an alarm.

As will become apparent from the description below, user input to definethe detection vector may comprise one or multiple user inputs. The oneor more user inputs may be received through one or more inputs of apointing device (e.g., a mouse) on the graphical user interface, one ormore inputs through a touch screen or similar device, one or more inputsof coordinates of the detection vector, an input of an angle ofdirection with relation to an axis, and/or an input of a numerical valueto represent the length of the vector. The input of numerical valuesmay, e.g., be entered into a data input field provided by the graphicaluser interface.

Any combination of these inputs may be used by the user to define thedetection vector. For example, in one embodiment the user may useinteraction between a mouse and the graphical user interface to define adirection of a detection vector, while the displacement (or length)defined by the detection vector may be specified through a text inputvia the graphical user interface.

The detection vector is displayed, and in some cases overlayed, as anarrow 201 on the two-dimensional image. In one embodiment of theinvention, an operator selects a detection area, then presses one of thearrow buttons on the graphical user interface (or the mouse) to makethat area directional. The system is then to place a detection vectorarrow on the selected detection area.

For example, the detection vector may appear to sit on the imagedetection area 200 as in FIG. 3 a. In one embodiment, the end points ofthis flat arrow detection vector is mapped to corresponding points inthe detection area monitored by the surveillance camera, thereby todefine the direction and minimum displacement in the detection areamonitored.

Alternatively, the detection vector could be given a three-dimensionalappearance as in FIG. 3 b where the arrow 203 appears to sit on thevirtual three-dimensional detection area 202. In this case, the positionof the end points of the three-dimensional area is correlated with theimage detection area, thereby to enable the rendering of the detectionvector in the correct perspective.

The user can adjust the detection vector's angle by dragging itsend-points with a point and click device, such as a computer mouse. Asmentioned, other user input means, such as a touch screen, may also beused. Alternatively, an angle can be entered manually (numerically)through the graphical user interface (e.g., through a data input fieldpresented on the graphical user interface), and the arrow drawnautomatically on the detection area at that angle. For example, theangle may be specified with respect to an x or y-axis of atwo-dimensional image plane. Alternatively, the angle is specified interms of a navigational bearing in the three-dimensional space.

In another embodiment, the system may be configured to automaticallydetermine a detection vector to be perpendicular to a selected edge. Forexample, when configuring a detection area and detection vector, a usermay select one edge of the directional area, and have the system thenautomatically determine and use a direction that is at right-angles tothat edge for a detection vector associated with the detection area. Inone example, the system could automatically correct the direction of adetection vector already placed in the detection area to thisperpendicular direction, or alternatively, the system could restrict anydetection vectors created after selection of the particular edge to havea direction perpendicular to the edge. For example, the graphical userinterface could prompt a user or provide a user with an option to selecta detection area edge thereby to apply a right-angled direction to anedge to the detection vector. Any other suitable way of implementing therestriction of direction on a detection vector could also be employed.

In one embodiment, the system 12 determines the direction of the vectorby first computing the end points of the selected edge of the detectionarea, computing the direction of that edge, and computing a directionthat is at right-angles to that direction, while still remaining in theplane of the detection area. It will be appreciated that, whereappropriate, intermediate positions on the selected edge could also beused. The direction of the detection vector could be computed for thetwo-dimensional image detection area, giving a direction that appears tothe user as perpendicular to the edge, or it could be computed for thethree-dimensional detection area, in which case the vector will appearto the user as being in a perspective three-dimensional directionperpendicular to the three-dimensional edge.

In some example embodiments, the length of the detection vector arrow asdisplayed is not to scale. For example, the graphical representation ofthe detection vector as displayed on the image detection area, inparticular the length of the graphical representation of the detectionvector may not be to scale in instances where the length of the vectoris input as a numerical value through the graphical user interface.

Irrespective, the length of the detection vector can be stretched by anoperator using a mouse or other suitable input device. As the detectionvector arrow is stretched, a tooltip or other indicator displays inmetres (or feet) the length the arrow represents. The distancerepresented by the arrow length is used as the minimum distance for thisdirectional area regardless of the setting in the configuration file.

In one example embodiment, the length in real-world coordinates isneither computed nor displayed by the client interface. Rather, theimage coordinates of the detection vector arrow are sent to an analyticsengine forming part of the monitoring station, where the imagecoordinates are converted to a calibrated distance, whereafter it isused.

In some instances, the detection vector may also be specified asbidirectional. With a bidirectional vector, an alarm condition isactivated when it is determined that the progressive displacement of thetarget object exceeds the minimum displacement in either direction ofthe vector.

The surveillance camera continuously monitors its field of view, inparticular the detection area, for any target objects. Video motiondetection (VMD), as described above, may be used for such detection inthat picture elements are monitored to identify target objects. Objecttargets tend to show up in an image with a different intensity to thebackground scene in a captured image. Groups of pixels associated withan object target are also tracked from frame to frame to determine apath of motion. As the scene is typically calibrated, the size of theobject target, the distance it has traveled, as well as the directioncan be estimated from the tracked group of pixels.

Thus, when an object travels through the field of view of the camera,the central monitoring station 12 processes images of the field of viewcaptured by surveillance cameras at discrete time intervals, locates thetarget object in each image, and computes its trajectory so that thedistance it has traveled (its displacement) can be computed. If thetrajectory is computed on the virtual three-dimensional detection area,then the distance can be expressed in real-world dimensions such asmetres traveled.

FIG. 4 shows graphically how the distance may be computed with relationto displacement in two dimensions.

The target object is observed by the central monitoring station 12 atdiscrete locations 305, and the trajectory 300 is computed from these.The actual length of the trajectory 300 can be computed by summing thesegment lengths along path 302. However, if directional criteria areintroduced, the segment lengths must be projected onto a line 301 in thedirection of interest, and those projections summed giving a path length303. Thus, for a progressive displacement of the target object,components in the direction of the detection vector are determined,which components are used to determine whether the minimum displacementidentified by the detection vector has been exceeded.

These components may be determined by obtaining the dot products of therelevant vectors, for example determining the scalar projection (orscalar component) of a particular displacement in the direction of thedetection vector.

If the reverse direction is of interest, or the direction isbidirectional, then the distance 304 is also of interest. An alarmactivation is only to occur if the distance in the direction of thedetection vector exceeds the minimum distance 306.

In one example embodiment, where images of the field of view anddetection area are not monitored on a continuous basis, estimations maybe used between the various sample points. Estimating the target objectpath may include predicting travel along various sections of the targetobject path. For example, the estimation step may include predicting thetarget object path prior to the initial detection position of the targetobject by back-predicting where the target object may have entered thefield of view to the position of initial detection. Also, the targetobject path between discreet detections of the target object may bepredicted, while the target object path may also be predicted betweenthe final point and where the target object is likely to have left thefield of view.

Thus, and as shown in the example of FIG. 4, the minimum distance may bemet by a point 307 on the estimated trajectory 300 which is between thedetection points or observations 305. This estimation is important whenthe observations are infrequent because the object may travel through adetection area with few or no observations inside the area. Thecontinuum of points on trajectory 300, which will include points insidethe detection area, will still be used to determine if the minimumdistance has been exceeded. Note also that the estimated trajectory 300may extend before the first observation, or beyond the last observationto account for objects that enter or leave the scene.

If this example is applied to the scene depicted in FIG. 5 a, it isshown that an intruder 403 crossing from the road 400 across thedetection area in the direction 401 will travel further than a distance402 in the direction of interest. If this displacement exceeds theminimum distance, then the system detects the intruder in accordancewith the predetermined condition and an alarm may be activated.

The converse case is depicted in FIG. 5 b. Here, a car and its shadow408 travel a distance 406, but in a direction that is not parallel tothe direction of the detection vector 405. Despite the distance traveledby the car and shadow 408, the only distance traveled in the directionof the detection vector is shown by reference 407. If this is less thanthe minimum distance for detection area 404, then the movement of thetarget object will not be permitted to raise an alarm. This effectivelyreduces this cause of false alarms.

To aid the user in defining a detection area that is longer than theminimum distance in the direction of interest, the scene scale atdifferent points in the image could be drawn. This is shown in FIG. 6where the horizontal distance 500 is displayed when the height 501 isbeing measured on a target in the scene. By doing this in more than onelocation, the horizontal distances 500 and 502 at different locations inthe scene can be established.

The use of a planar detection area in the following description is notintended to exclude other possible detection area surfaces in thethree-dimensional space, nor does it imply that it is necessary toapproximate the detection area by a planar surface. The ground in a realscene is typically uneven, and the invention only requires knowing themapping between that scene and the two-dimensional image of the scene.

One issue with requiring a minimum distance to be met is that it isdifficult to estimate that length when drawing the detection area on thecomputer screen. Another example provides feedback to the operator sothat the operator can accurately adjust the size of the detection areaboth in the near-field and in the far-field of the scene that is beingmonitored. The following aspects relate to this enhancement.

The detection vector appears to sit on the three-dimensional detectionarea with its length representing the minimum distance. In one instancethe minimum distance is set by a numerical value, and the length of thedetection vector arrow adjusts automatically as the detection vectorarrow is manipulated so as to always show the correctly scaled minimumdistance in the scene wherever it is placed on the computer screen. Thescale changes as the detection vector arrow is moved through the scenebecause of scene perspective. In another instance, the length of thearrow is set by manipulating the end points of the arrow using acomputer mouse. If it is then moved, the length adjusts automatically soas to always show the minimum distance wherever it is placed in theimage.

In another instance, the feedback is provided when the scene is beingcalibrated. To provide enough information for the analytics engine ofthe monitoring station to determine the size and speed of targets in theimage, it is necessary for the user to calibrate the scene. In thisinstance, as the user marks out the height of an object, a horizontalline of that length is displayed simultaneously. It is then clear to theuser what that dimension looks like at that position in the image. Sincecalibration requires more than one measurement, the operator will beshown this dimension in more than one position in the image.

Transformations between a two-dimensional image and a three-dimensionalscene as well as calculations to determine whether a minimum distance inthe direction of a detection vector has been traveled are now describedin more detail. In a particular scenario a transformation matrix T isused to transform a point i in two-dimensional image coordinates into apoint s in three-dimensional scene coordinates. If we then consider twopoints i₁ and i₂ in this two dimensional image which correspond to twopoints on a path P of an object, it follows that a two-dimensionalvector I joining points i₁ and i₂ could be transformed into athree-dimensional vector {right arrow over (S)}, joining points s₁ ands₂ as follows:

s ₁ =Ti ₁

s ₂ =Ti ₂

Therefore: I→{right arrow over (S)}

This vector {right arrow over (S)} represents the distance and directionthe object moved in three-dimensional coordinates between the twoobservations.

A directional area (detection area) where a user manipulates an arrow intwo-dimensional image space, i.e. a detection vector, to specify thedirection of that area is now considered. The endpoints of this arrowcan be transformed to the three-dimensional space using the sametransformation T to produce a directional vector {right arrow over (D)}.The unity vector representing the direction of {right arrow over (D)}can be determined by dividing {right arrow over (D)} by its magnitude|D|.

The component I of the length of {right arrow over (S)} that lies in thedirection of to {right arrow over (D)} can be calculated using the dotproduct (·) between {right arrow over (S)} and the unity vectorcorresponding to {right arrow over (D)} as follows:

$I = \frac{\overset{\rightarrow}{S} \cdot \overset{\rightarrow}{D}}{D}$

Note that the polarity of the result I is important as only valuesgreater than zero are distances in the direction of interest. Valuesless than zero are in the opposite direction. If these distances I fromconsecutive segments of path P are summed together, taking account ofthe polarity of each I, then the combined distance of those segmentsalong P in the direction of interest can be computed. If the distanceexceeds the minimum distance, then the target may trigger an alarm.

Note that the computation can be performed between every correspondingimage pixel of path P, but if the path is comprised of line segmentsthen the computation can be simplified by only considering the endpoints of those line segments and the points where the path intersectswith the entry and exit pixels in the detection area.

A First Worked Example

A working example is now described, to illustrate how an intrusiondetection system may operate.

The system employs a sensing device in the form of a video camera whichis used to view and monitor a real scene (in the form of a field ofview) such as the three-dimensional scene depicted in FIG. 2 b. Theimage taken by the camera is a two-dimensional projection of thatthree-dimensional scene onto the image plane of the camera. The cameraimage is then displayed on a computer monitor or display.

To use the system, an operator defines a detection area, such as 102 inFIG. 2 a, through interactions with a graphical user interface on thecomputer monitor. This corresponds to a virtual planar detection area inthe three-dimensional scene, such as 105 in FIG. 2 b.

There are many ways to calibrate the system, but for illustrativepurposes, calibration occurs while a person walks around every part ofthe three-dimensional planar detection area 105, and their location onboth the detection area 102 and the virtual image detection area 105 isrecorded at every point. It will be appreciated that this establishesthe mapping between the two detection areas. It will be appreciated thatin some embodiments, areas outside the detection areas may also becalibrated.

Typically a moving target object differs in intensity from thebackground in front of which it moves. Thus, such an object can bedetected by thresholding the difference between this image and anearlier image, and the object can then be tracked by first drawing abounding box around the set of image pixels detected by this thresholdeddifference and then noting the position of the bottom centre of thebounding box as it moves from frame to frame of the video sequence. Forevery image captured by the video camera, the object is detected and itslocation noted (and thus tracked). The path of the target object can beestimated by a curve drawn through these points, or more simply as aseries of line segments joining these points. This path is used toestimate where the object was in the detection area and if the objecthas traveled far enough in the direction of interest. It is important toconsider the full estimated path because a fast moving object may onlybe detected in a few frames of video and provide insufficient points forreliable detection.

If an intruder now passes through the scene, and walks on the planardetection area 105, then the path of their feet in three-dimensionalcoordinates can be determined from the path of the image of their feetin the detection area 102. The distance, speed and direction of travelin three dimensions can all be determined readily from the correspondingmetrics in image coordinates.

If a directional area is used, i.e. if a user has defined a detectionvector for a detection area, then the direction for that directionalarea is specified in image coordinates. Three-dimensional coordinatescan be determined from these image coordinates, or alternatively, thedetection vector may be defined in three-dimensional coordinates, inwhich case the image coordinates can be determined. Since the mappingbetween the detection planes is defined, the appearance of athree-dimensional arrow sitting on the three-dimensional plane can alsobe determined by taking into account the scene perspective and scaling.If the detection vector arrow is moved up the screen then it could bemade to appear to move away from the camera by reducing its size. If thearrow is rotated, then the correct three-dimensional rotation could bedetermined and the three-dimensional arrow adjusted accordingly.

Now, if an intruder walks across the three-dimensional detection areamonitored by the camera, it is clear that the direction in thethree-dimensional coordinate system and the direction specified in termsof the directional area in three-dimensional coordinates can both bedetermined. The distance traveled in the direction of the directionalarea may then be established by determining a component of thedisplacement in the direction of the detection vector. If that distanceexceeds the minimum displacement, then an alarm condition may be raised.By this example it is clear that the system distinguishes between anintruder approaching the protected property and a vehicle passing by.

A Second Worked Example

FIG. 7 shows how the entry and exit from a detection area can bedetermined. In this Figure, the two-dimensional image 600 consists ofpicture elements (pixels) 601, and the detection area is defined by theshaded pixels 602. The path 603 of an object passes through thedetection area 602, entering it at a first pixel 604 and exiting it at afinal pixel 605. The three-dimensional coordinates of pixels 604 and 605can be computed from the two-dimensional positions in the image (asdiscussed in detail above), and the three-dimensional coordinates maythen be used to determine the component of the three-dimensionaldistance traveled in the direction specified. Furthermore, any or allother pixels 606 along the path inside the detection area may besimilarly transformed and considered. This is particularly important ifthe path is not straight.

If the detection area 602 above is narrow enough where the object pathpasses through, then the pixels 604 and 605 may be the same pixel. Inthis cast, the object path is still determined and if the path passesthrough the area in the correct direction, then it may trigger an alarm.If the object path passes through the area in the wrong direction, analarm will not be triggered. Thus, even with a minimum distance of zero,and/or an area that is only one pixel wide, the direction can still berelevant to detect an intrusion.

Turning now to FIG. 8, an image detection area 701 is shown as displayedon an example graphical user interface 700. The image detection area 701is indicated in cross-hatching over an image 702 of the field of view ofa sensing device such as a surveillance camera, wherein the image 702shows a road leading to a building in the background, with the roadhaving an adjacent gravel area to the right-hand side of the road. Alsoindicated is a detection vector 703 which is associated with the imagedetection area 701 and overlayed on the detection area 701. As isdescribed in more detail above, the detection vector 703 indicates aminimum distance and direction of displacement in the image detectionarea 701, which in this instance is displacement in thethree-dimensional area extending from an edge to the left-hand side ofthe road, across the road and the adjacent gravel area. The direction ofthe detection vector is indicated as being at a slight angle to thedirection of directly crossing the road. In the event that an intruder(i.e. a target object) moves across this detection area 701, e.g.,partially across the road or across the gravel area, the present systemis to calculate a component of displacement of such target object in thedirection of the detection vector 703, and if this component exceeds thespecified minimum displacement in the direction of the detection vector703, an alarm will be sounded. In order to calculate this displacement,three-dimensional coordinates of pixels of the target object arecomputed from the two-dimensional positions in the image, with thethree-dimensional coordinates then being used to determine theparticular component of the three-dimensional distance traveled in thedirection specified.

FIG. 9 shows a similar graphical user interface 800 which shows asurveillance camera image 802 of an office environment. An imagedetection area 801 is again shown as cross-hatching over the image 802while a detection vector 803 is overlayed as an arrow over the detectionarea 801. The detection vector 803 indicates a minimum distance anddirection of displacement in the image detection area 802 asdisplacement in the three-dimensional area of the office. Should anintruder (i.e. a target object) move through the room, e.g., from theback of the room to the front of the room along the glass partition, orvice versa, an alarm will be sounded if the system determines that thedisplacement of the intruder in the detection area 801 has a componentin the direction of the detection vector which exceeds the specifiedminimum displacement of the detection vector. As the detection vectorapplies to the entire detection area 802, it will be appreciated thatany movement by an intruder that is calculated to have a component inthe direction of the detection vector which exceeds the specifiedminimum level will result in an alarm activation.

Similar to the scenario depicted in FIG. 8, the calculations includecomputing three-dimensional coordinates of pixels of the target objectfrom the two-dimensional positions in the image, then using thesecoordinates to determine the particular component of thethree-dimensional distance traveled in the direction specified.

FIG. 10 is a block diagram illustrating a typical computer processingsystem 900 suitable for use/configuration as the central monitoringsystem 12 as described above. For example, the typical computer systemmay be suitable for both the server 16 of the central monitoring system12 as well as the operator stations 22.1, 22.2.

Computer processing system 900 comprises a processing unit 902. Theprocessing unit 902 may comprise a single computer-processing device(e.g. a central processing unit, graphics processing unit, or othercomputational device), or may comprise a plurality of computerprocessing devices. In some instances processing is performed solely byprocessing unit 902, however in other instances processing may also, oralternatively, be performed by remote processing devices accessible anduseable (either in a shared or dedicated manner) by the computerprocessing system 900.

Through a communications bus 904 the processing unit 902 is in datacommunication with one or more machine-readable storage (memory) devicesthat store instructions and/or data for controlling operation of thecomputer processing system 900. In this instance computer processingsystem 900 comprises a system memory 906 (e.g. a BIOS or flash memory),volatile memory 908 (e.g. random access memory such as one or more DRAMmodules), and non-volatile/non-transient memory 910 (e.g. one or morehard disk or solid state drives).

Computer processing system 900 also comprises one or more interfaces,indicated generally by 912, via which the computer processing system 900interfaces with various components, other devices and/or networks. Othercomponents/devices may be physically integrated with the computerprocessing system 900, or may be physically separate. Where such devicesare physically separate connection with the computer processing system400 may be via wired or wireless hardware and communication protocols,and may be direct or indirect (e.g., networked) connections.

Wired connection with other devices/networks may be by any standard orproprietary hardware and connectivity protocols. For example, thecomputer processing system 900 may be configured for wired connectionwith other devices/communications networks by one or more of: USB;FireWire; eSATA; Thunderbolt; Ethernet; Parallel; Serial; HDMI; DVI;VGA; AudioPort. Other wired connections are possible.

Wireless connection with other devices/networks may similarly be by anystandard or proprietary hardware and communications protocols. Forexample, the computer processing system 400 may be configured forwireless connection with other devices/communications networks using oneor more of: infrared; Bluetooth (including early versions of Bluetooth,Bluetooth 4.0/4.1/4.2 (also known as Bluetooth low energy) and futureBluetooth versions); Wi-Fi; near field communications (NFC); GlobalSystem for Mobile Communications (GSM), Enhanced Data GSM Environment(EDGE), long term evolution (LTE), wideband code division multipleaccess (W-CDMA), code division multiple access (CDMA). Other wirelessconnections are possible.

Generally speaking, the devices to which computer processing system 900connects—whether by wired or wireless means—allow data to be inputinto/received by computer processing system 900 for processing by theprocessing unit 902, and data to be output by computer processing system900. Example devices are described below, however it will be appreciatedthat not all computer processing systems will comprise all mentioneddevices, and that additional and alternative devices to those mentionedmay well be used.

For example, computer processing system 900 may comprise or connect toone or more input devices by which information/data is input into(received by) the computer processing system 900. Such input devices maycomprise physical buttons, alphanumeric input devices (e.g., keyboards),pointing devices (e.g., mice, track-pads and the like), touchscreens,touchscreen displays, microphones, accelerometers, proximity sensors,GPS devices and the like. Computer processing system 900 may alsocomprise or connect to one or more output devices controlled by computerprocessing system 900 to output information. Such output devices maycomprise devices such as indicators (e.g., LED, LCD or other lights),displays (e.g., LCD displays, LED displays, plasma displays, touchscreen displays), audio output devices such as speakers, vibrationmodules, and other output devices. Computer processing system 400 mayalso comprise or connect to devices capable of being both input andoutput devices, for example memory devices (hard drives, solid statedrives, disk drives, compact flash cards, SD cards and the like) whichcomputer processing system 400 can read data from and/or write data to,and touchscreen displays which can both display (output) data andreceive touch signals (input).

Computer processing system 900 may also connect to communicationsnetworks (e.g. the Internet, a local area network, a wide area network,a personal hotspot etc.) to communicate data to and receive data fromnetworked devices, which may be other computer processing systems.

The architecture depicted in FIG. 10 may be implemented in a variety ofcomputer processing systems, for example a laptop computer, a netbookcomputer, a tablet computer, a smart phone, a desktop computer, a servercomputer. It will also be appreciated that FIG. 10 does not illustrateall functional or physical components of a computer processing system.For example, no power supply or power supply interface has beendepicted, however computer processing system 900 will carry a powersupply (e.g. a battery) and/or be connectable to a power supply. It willfurther be appreciated that the particular type of computer processingsystem will determine the appropriate hardware and architecture, andalternative computer processing systems may have additional,alternative, or fewer components than those depicted, combine two ormore components, and/or have a different configuration or arrangement ofcomponents.

Operation of the computer processing system 900 is also caused by one ormore computer program modules which configure computer processing system900 to receive, process, and output data. One such computer programmodule will be an operating system such as (by way of non-limitingexample) Apple iOS or Android.

As used herein, the term “module” to refers to computer programinstruction and other logic for providing a specified functionality. Amodule can be implemented in hardware, firmware, and/or software. Amodule is typically stored on the storage device 908, loaded into thememory 906, and executed by the processor 902.

A module can include one or more processes, and/or be provided by onlypart of a process. Embodiments of the entities described herein caninclude other and/or different modules than the ones described here. Inaddition, the functionality attributed to the modules can be performedby other or different modules in other embodiments. Moreover, thisdescription occasionally omits the term “module” for purposes of clarityand convenience.

It will be appreciated that the types of computer systems 900 used bythe respective entities of FIG. 1 may vary depending upon the embodimentand the processing power used by the entity. For example, the serversystems may comprise multiple blade servers working together to providethe functionality described herein.

What is claimed is:
 1. A method of intrusion detection, the methodincluding: displaying sensing device output data as one or more imagesof a field of view of a sensing device; through a graphical userinterface: receiving user input to define and display an image detectionarea in relation to the one or more images of the field of view, whereinthe image detection area is correlated to a detection area to bemonitored by the sensing device for intrusion detection; and receivinguser input that defines a detection vector specifying a predeterminedminimum displacement and direction of displacement in the field of viewto be monitored, wherein a graphical representation of the detectionvector is displayed on the image detection area; detecting a targetobject; and tracking the target object at least partially through thedetection area and determining whether the progressive displacement ofthe target object has a component which exceeds the minimum displacementin the direction of the detection vector.
 2. The method as claimed inclaim 1, further including activating an alarm condition if the targetobject's displacement has a component which is determined to exceed theminimum displacement in the direction of the detection vector, whereinat least one of an initial position of the target object or the positionof the target object where the minimum displacement is exceeded, iswithin the detection area.
 3. The method as claimed in claim 1, furtherincluding activating an alarm condition if the target object'sdisplacement is only within the detection area and the target object'sdisplacement exceeds the minimum displacement.
 4. The method as claimedin claim 2 wherein the component of displacement in the direction of thedetection vector is determined with the use of the equation:$I = \frac{\overset{\rightarrow}{S} \cdot \overset{\rightarrow}{D}}{D}$where I is the component of displacement in the direction of thedetection vector; vector {right arrow over (S)} represents a distanceand direction of the target object as tracked through the detection areain a three-dimensional area; and vector {right arrow over (D)}represents the detection vector.
 5. The method as claimed in claim 1wherein the image detection area is a surface in a three-dimensionalspace onto which object movements in the corresponding two-dimensionalimage are projected and interpreted.
 6. The method as claimed in claim 1wherein the detection area is defined as directional by specifying thedetection vector in relation to the detection area.
 7. The method asclaimed in claim 6 wherein the detection vector is specified asbidirectional and an alarm condition is activated when it is determinedthat the progressive displacement of the target object exceeds theminimum displacement in either direction of the vector.
 8. The method asclaimed in claim 1 wherein the graphical representation of the detectionvector is overlaid on the image detection area as a flat arrow on atwo-dimensional image plane.
 9. The method as claimed in claim 8 whereinthe end points of this flat arrow detection vector is mapped tocorresponding points in the detection area monitored by the sensingdevice, thereby to define the direction and minimum displacement in thedetection area monitored.
 10. The method as claimed in claim 1 whereinthe graphical representation of the detection vector is overlaid on theimage detection area as a three-dimensional arrow detection vectorwithin the detection area monitored, wherein the position of the endpoints of the three-dimensional area is correlated with the imagedetection area, thereby to enable the rendering of the detection vectorin the correct perspective.
 11. The method as claimed in claim 1 whereinthe one or more user inputs is received through one or more inputs of apointing device on the graphical user interface, one or more inputsthrough a touch screen, one or more inputs of coordinates of thedetection vector, an input of an angle of direction with relation to anaxis, and/or an input of a numerical value.
 12. The method as claimed inclaim 1 wherein the displacement and/or direction of the detectionvector is defined or adjusted through manipulation of arrows via thegraphical user interface.
 13. The method as claimed in claim 1 whereinthe displacement and/or direction of the detection vector is defined oradjusted by receiving a numerical value for the length and/or angle ofdirection of the detection vector from a user through the graphical userinterface.
 14. The method as claimed in claim 1 wherein the angle ofdirection of the detection vector is specified in terms of anavigational bearing in a three-dimensional area.
 15. The method asclaimed in claim 1 wherein the direction of the detection vector isdetermined as perpendicular to a particular edge of the detection area,after the user has selected the particular detection area edge throughthe graphical user interface.
 16. The method as claimed in claim 1wherein the length of the graphical representation of the detectionvector as displayed on the image detection area is not to scale.
 17. Themethod as claimed in claim 1 wherein the sensing device is asurveillance camera.
 18. The method as claimed in claim 1 wherein thestep of tracking the target object at least partially through thedetection area includes estimating a target object path from discreetdetections of the target object within the field of view within thedetection area of the sensing device.
 19. The method as claimed in claim18 wherein the step of estimating the target object path furtherincludes predicting various sections of the target object path.
 20. Themethod as claimed in claim 18 wherein the step of predicting varioussections of the target object path includes one or more of thefollowing: predicting the target object path prior to initial detectionof the target object by back-predicting where the target object may haveentered the field of view to the position of initial detection;predicting the target object path between discreet detections of thetarget object; and predicting the target object path between a finalpoint of detection of the target object in the field of view and alikely position where the target object may have left the field of view.21. A system including: a user interface in order to receive inputs froma user of the system; at least one processing unit and at least onememory for storing instructions for execution by the at least oneprocessing unit, the instructions executed to: display sensing deviceoutput data as one or more images of a field of view of a sensingdevice; through a graphical user interface: receive user input to defineand display an image detection area in relation to the one or moreimages of the field of view, wherein the image detection area iscorrelated to a detection area to be monitored by the sensing device forintrusion detection; and receive user input that specifies a detectionvector specifying a predetermined minimum displacement and direction ofdisplacement in the field of view to be monitored, wherein a graphicalrepresentation of the detection vector is displayed on the imagedetection area; detect a target object; and track the target object atleast partially through the detection area and determining whether theprogressive displacement of the target object has a component whichexceeds the minimum displacement in the direction of the detectionvector.
 22. The system as claimed in claim 21, further wherein theinstructions are executed to activate an alarm condition if the targetobject's displacement has a component which is determined to exceed theminimum displacement in the direction of the detection vector, whereinat least one of an initial position of the target object or the positionof the target object where the minimum displacement is exceeded, iswithin the detection area.
 23. The system as claimed in claim 21,further wherein the instructions are executed to activate an alarmcondition if the target object's displacement is only within thedetection area and the target object's displacement exceeds the minimumdisplacement.
 24. The system as claimed in claim 22, wherein thecomponent of displacement in the direction of the detection vector isdetermined with the use of the equation:$I = \frac{\overset{\rightarrow}{S} \cdot \overset{\rightarrow}{D}}{D}$where I is the component of displacement in the direction of thedetection vector; vector {right arrow over (S)} represents a distanceand direction of the target object as tracked through the detection areain a three-dimensional area; and vector {right arrow over (D)}represents the detection vector.
 25. The system as claimed in claim 21,wherein the image detection area is a surface in a three-dimensionalspace onto which object movements in the corresponding two-dimensionalimage are projected and interpreted.